Extension procedures for lattice Lipschitz operators on Euclidean spaces

Author:

Arnau RogerORCID,Calabuig J. M.ORCID,Erdoğan EzgiORCID,Sánchez Pérez Enrique A.ORCID

Abstract

AbstractWe present a new class of Lipschitz operators on Euclidean lattices that we call lattice Lipschitz maps, and we prove that the associated McShane and Whitney formulas provide the same extension result that holds for the real valued case. Essentially, these maps satisfy a (vector-valued) Lipschitz inequality involving the order of the lattice, with the peculiarity that the usual Lipschitz constant becomes a positive real function. Our main result shows that, in the case of Euclidean space, being lattice Lipschitz is equivalent to having a diagonal representation, in which the coordinate coefficients are real-valued Lipschitz functions. We also show that in the linear case the extension of a diagonalizable operator from the values in their eigenvectors coincide with the operator obtained both from the McShane and the Whitney formulae. Our work on such extension/representation formulas is intended to follow current research on the design of machine learning algorithms based on the extension of Lipschitz functions.

Funder

Universidad Politècnica de València

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Geometry and Topology,Algebra and Number Theory,Analysis

Reference25 articles.

1. Aliprantis, C.D., Burkinshaw, O.: Positive Operators. Springer, Berlin (2006)

2. Anil, C., Lucas, J., Grosse, R.: Sorting out Lipschitz function approximation. In: Proceedings of the 36th International Conference on Machine Learning, PMLR 97, pp. 291–301 (2019)

3. Appell, J., De Pascale, E., Vignoli, A.: Nonlinear Spectral Theory. Walter de Gruyter, Berlin (2008)

4. Asadi, K., Misra, D., Littman, M.: Lipschitz continuity in model-based reinforcement learning. In: Proceedings of the 35th International Conference on Machine Learning, PMLR 80, pp. 264–273 (2018)

5. Calabuig, J.M., Falciani, H., Sánchez Pérez, E.A.: Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets. Neurocomputing 398, 172–184 (2020)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Approximation of Almost Diagonal Non-linear Maps by Lattice Lipschitz Operators;Bulletin of the Brazilian Mathematical Society, New Series;2024-02-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3